In the field of imaging, images recorded on conventional supports may be renovated or restored by processing a previously digitized version of these images. The conventional supports for such images generally include film, photographic paper or magnetic tape. Restoration of these images recovers the representation of the original image, which may have been damaged because of aging for example, or other damage due to the processing or handling of the photographic paper, film or magnetic supports. The images to be restored may be still or animated. Still images are generally photographs representing, for example, people landscapes or photographs of prints (drawings, tables, etc.). Animated images are generally film sequences; for example motion pictures or video sequences, for example from televised documents or amateur videos. Digital restoration methods known to those skilled in the art, generally includes digitizing the image originally recorded on a silver, film or magnetic support, and then processing the original digitized image to restore it. Such methods include transforming the original digitized image according to instructions given by the algorithms of the image processing software. The now transformed or restored digitized image can be memorized and used later in digital form or, for example, printed out on film or photographic paper.
Several techniques for the restoration or elimination of scratches in digital images are known to those skilled in the art and have been published. Methods for eliminating scratches are generally used to restore motion pictures. The disclosure of the following articles may be quoted as examples: “Detection and removal of line scratches in motion picture films” (authors: B. Besserer, S. Boukir, O. Buisson, L. Joyeux), pages 548 to 553, published in June 1999, CVPR'99, IEEE International Conference On Computer Vision and Pattern Recognition, Fort Collins, Colo. “Computerized motion picture restoration system” (authors: M. N. Chong, S. Kalra, S. Krishnan, A. Laud), pages 153 to 159, published in June 1998, Broadcast Asia International Conference, Singapore. Or “Restauration automatique de films anciens” (author: E. Decencière-Ferrandière), thesis paper published in November 1997, Ecole Normale Supérieure des Mines in Paris. Such methods may be used to remove scratches from a digitized image, but they are not capable of fully restoring such images. Furthermore, they are often rather slow when used for films containing high-resolution images: resolution being the density of points or lines (expressed as pixels per centimeter, for example).
The article “Film line scratch removal using Kalman filtering and Bayesian restoration” (authors: B. Besserer, S. Boukir, L. Joyeux), published in December 2000, WACV'2000, IEEE Workshop on the Application of Computer Vision, Palm Springs, Calif., describes a method for the automatic elimination of vertical scratches in sequences of digital images (video, film). Two processes are performed in succession: restoration is first carried out at low resolution through the use of cubic polynomial interpolation, the parameters of which are estimated using the method of least squares. Restoration is then completed by high-resolution analysis, exploiting statistical models based on Markovian fields and associated with a decision-making criterion of the Maximum A Posteriori (MAP) type. This space-time method is relatively complex and uses a large number of parameters.
Scratches can be restored in an average time of approximately 2 seconds per scratch.
The processes used in the methods above do not generally use the residual information likely to be present in each of the altered areas of the image to restore it. The residual information of the image is that part of the original signal (including the grain) which has not been affected by deterioration due to scratching. The methods used are therefore based on the hypothesis that the image data is missing. The missing digital data correspond to the intensity of the pixels of the image on the scratch. Therefore, these methods use mathematical models (probabilistic for example) of greater or less complexity; they are often based on a space-time analysis (for example, the use of data from the image before and after the image affected by scratches), and require the use of special, costly hardware (Silicon Graphics or Sun workstations, for example). The fact of not using the residual signal in the altered areas of the image, makes the restoration method random and more or less effective, depending on the texture (homogeneity of the intensity of the gray levels or colors) of the altered image on the scratch of the image.
The disadvantage of such methods that do not use or exploit the residual information in the scratch area is that they do not take into account the real alteration of the coat of emulsion containing the original image. This makes such restoration methods relatively ineffective and time consuming; for example, it may be necessary to add grain after restoring the texture of the original image in the altered area. A significant spatial extension of scratches in an image and their persistence in time, in the case of sequences of animated images, makes the human eye particularly sensitive to such scratches. In particular, scratches made on successive images, which are non-homogenous in texture, of a copy of film being projected may be detected by the human eye, even after restoration using these older techniques. Such restoration methods do not generally take into account the residual information of the image in the altered areas and are not therefore robust enough. Therefore, the human eye will still detect scratches the elimination of which after restoration is more or less effective. For example, experience shows that in a sequence of several dozen animated images projected in a few seconds, the human eye remains sensitive to the slightest trace of scratching.